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At least 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Development of a Scalable Risk-informed Predictive Maintenance Cloud-based Strategy at Nuclear Power Plants

The fact that light-water reactor operation and maintenance costs are prohibitively expensive and contribute to the premature decommissioning of nuclear power plants is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology, as its low cost, computing and storage adaptability, and ability to host applications across numerous virtual infrastructures potentially make it a cost-effective alternative to onsite storage and diagnostics. In this paper, a technological assessment is carried out on a provisional cloud deployment architecture for a nuclear power plant predictive monitoring system. This cloud-based monitoring system would enable maintenance and diagnostic analysts and other authorized plant users to remotely monitor equipment functionality, thus enabling early fault detection and effective predictive maintenance practices. To provide data processing and storage, sensor device networking, and database management, the Microsoft Azure cloud platform is utilized as part of the proposed cloud architecture; however, this analysis could be extended to other cloud computing service providers as well. The focus of this paper is on application of cloud resources for enabling predictive maintenance, identification of technological hurdles associated with moving to a cloud-computing-based architecture, and potential benefits from moving to a centralized cloud system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A Conceptual Architecture for a Quantum-HPC Middleware

Abstract-Quantum computing promises potential for science and industry by solving certain computationally complex problems faster than classical computers. Quantum computing systems evolved from coupled to classical computing nodes (HPC). With the increasing monolithic systems towards modular architectures comprising multiple quantum processing units (QPUs) scale, middleware systems that facilitate the efficient coupling of quantum-classical computing are becoming critical. Through an in-depth analysis of quantum applications, integration patterns and systems, we identified a gap in understanding Quantum HPC middleware systems. We present a conceptual middleware to facilitate reasoning about quantum-classical integration and serve as the basis for a future middleware system. An essential contribution of this paper lies in leveraging well-established high performance computing abstractions for managing workloads, tasks, and resources to integrate quantum computing into HPC systems seamlessly

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

A Global Repository for Planet-Sized Experiments and Observations

Working across U.S. federal agencies, international agencies, and multiple worldwide data centers, and spanning seven international network organizations, the Earth System Grid Federation (ESGF) allows users to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a system of geographically distributed peer nodes that are independently administered yet united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP) output used by the Intergovernmental Panel on Climate Change assessment reports. Data served by ESGF not only include model output (i.e., CMIP simulation runs) but also include observational data from satellites and instruments, reanalyses, and generated images. Metadata summarize basic information about the data for fast and easy data discovery.

Earth Systems Grid Federation (ESGFC)↗
DOE OSTI2016

View factor modeling of sputter-deposition on micron-scale-architectured surfaces exposed to plasma

The sputter-deposition on surfaces exposed to plasma plays an important role in the erosion behavior and overall performance of a wide range of plasma devices. Plasma models in the low density, low energy plasma regime typically neglect micron-scale surface feature effects on the net sputter yield and erosion rate. The model discussed in this paper captures such surface architecture effects via a computationally efficient view factor model. The model compares well with experimental measurements of argon ion sputter yield from a nickel surface with a triangle wave geometry with peak heights in the hundreds of microns range. Further analysis with the model shows that increasing the surface pitch angle beyond about 45° can lead to significant decreases in the normalized net sputter yield for all simulated ion incident energies (i.e., 75, 100, 200, and 400 eV) for both smooth and roughened surfaces. At higher incident energies, smooth triangular surfaces exhibit a nonmonotonic trend in the normalized net sputter yield with surface pitch angle with a maximum yield above unity over a range of intermediate angles. The resulting increased erosion rate occurs because increased sputter yield due to the local ion incidence angle outweighs increased deposition due to the sputterant angular distribution. The model also compares well with experimentally observed radial expansion of protuberances (measuring tens of microns) in a nano-rod field exposed to an argon beam. The model captures the coalescence of sputterants at the protuberance sites and accurately illustrates the structure's expansion due to deposition from surrounding sputtering surfaces; these capabilities will be used for future studies into more complex surface architectures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

NWChem: Quantum Chemistry Simulations at Scale

Methods based on quantum mechanics equations have been developed since the 1930's with the purpose of accurately studying the electronic structure of molecules. However, it is only during the last two decades that intense development of new computational algorithms has opened the possibility of performing accurate simulations of challenging molecular processes with high-order many-body methods. A wealth of evidence indicates that the proper inclusion of instantaneous interactions between electrons (or the so-called electron correlation effects) is indispensable for the accurate characterization of chemical reactivity, molecular properties, and interactions of light with matter. The availability of reliable methods for benchmarking of medium-size molecular systems provides also a unique chance to propagate high-level accuracy across spatial scales through the multiscale methodologies. Some of these methods have potential to utilize computational resources in an effi*cient way since they are characterized by high numerical complexity and appropriate level of data granularity, which can be effi*ciently distributed over multi-processor architectures. The broad spectrum of coupled cluster (CC) methods falls into this class of methodologies. Several recent CC implementations clearly demonstrated the scalability of CC formalisms on architectures composed of hundreds thousand computational cores. In this context NWChem provides a collection of Tensor Contraction Engine (TCE) generated parallel implementations of various coupled cluster methods capable of taking advantage of many thousand of cores on leadership class parallel architectures.

chemistry↗
DOE OSTI2020

Measuring success for a future vision: Defining impact in science gateways/virtual research environments

Scholars worldwide leverage science gateways/virtual research environments (VREs) for a wide variety of research and education endeavors spanning diverse scientific fields. Evaluating the value of a given science gateway/VRE to its constituent community is critical in obtaining the financial and human resources necessary to sustain operations and increase adoption in the user community. In this article, we feature a variety of exemplar science gateways/VREs and detail how they define impact in terms of, for example, their purpose, operation principles, and size of user base. Further, the exemplars recognize that their science gateways/VREs will continuously evolve with technological advancements and standards in cloud computing platforms, web service architectures, data management tools and cybersecurity. We also present a number of technology advances that could be incorporated in next-generation science gateways/VREs to enhance their scope and scale of their operations for greater success/impact. The exemplars are selected from owners of science gateways in the Science Gateways Community Institute (SGCI) clientele in the United States, and from the owners of VREs in the International Virtual Research Environment Interest Group (VRE-IG) of the Research Data Alliance. Thus, community-driven best practices and technology advances are compiled from diverse expert groups with an international perspective to envisage futuristic science gateway/VRE innovations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Sustainable Cooperative Robotic Technologies for Human and Robotic Outpost Infrastructure Construction and Maintenance

Robotic Construction Crew (RCC) is a heterogeneous multi-robot system for autonomous acquisition, transport, and precision mating of components in construction tasks. RCC minimizes resources constrained in a space environment such as computation, power, communication and, sensing. A behavior-based architecture provides adaptability and robustness despite low computational requirements. RCC successfully performs several construction related tasks in an emulated outdoor environment despite high levels of uncertainty in motions and sensing. Quantitative results are provided for formation keeping in component transport, precision instrument placement, and construction tasks.

multi-robot systems↗
NASA NTRS2007

Grid Task Execution

IPG Execution Service is a framework that reliably executes complex jobs on a computational grid, and is part of the IPG service architecture designed to support location-independent computing. The new grid service enables users to describe the platform on which they need a job to run, which allows the service to locate the desired platform, configure it for the required application, and execute the job. After a job is submitted, users can monitor it through periodic notifications, or through queries. Each job consists of a set of tasks that performs actions such as executing applications and managing data. Each task is executed based on a starting condition that is an expression of the states of other tasks. This formulation allows tasks to be executed in parallel, and also allows a user to specify tasks to execute when other tasks succeed, fail, or are canceled. The two core components of the Execution Service are the Task Database, which stores tasks that have been submitted for execution, and the Task Manager, which executes tasks in the proper order, based on the user-specified starting conditions, and avoids overloading local and remote resources while executing tasks.

Hu, Chaumin↗
DOE OSTI2021

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Reconfigurable Framework for Resilient Semantic Segmentation for Space Applications

Deep learning (DL) presents new opportunities for enabling spacecraft autonomy, onboard analysis, and intelligent applications for space missions. However, DL applications are computationally intensive and often infeasible to deploy on radiation-hardened (rad-hard) processors, which traditionally harness a fraction of the computational capability of their commercial-off-the-shelf counterparts. Commercial FPGAs and system-on-chips present numerous architectural advantages and provide the computation capabilities to enable onboard DL applications; however, these devices are highly susceptible to radiation-induced single-event effects (SEEs) that can degrade the dependability of DL applications. In this article, we propose Reconfigurable ConvNet (RECON), a reconfigurable acceleration framework for dependable, high-performance semantic segmentation for space applications. In RECON, we propose both selective and adaptive approaches to enable efficient SEE mitigation. In our selective approach, control-flow parts are selectively protected by triple-modular redundancy to minimize SEE-induced hangs, and in our adaptive approach, partial reconfiguration is used to adapt the mitigation of dataflow parts in response to a dynamic radiation environment. Combined, both approaches enable RECON to maximize system performability subject to mission availability constraints. We perform fault injection and neutron irradiation to observe the susceptibility of RECON and use dependability modeling to evaluate RECON in various orbital case studies to demonstrate a 1.5–3.0× performability improvement in both performance and energy efficiency compared to static approaches.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1986

Systolic VLSI for Kalman filters

A novel two-dimensional parallel computing method for real-time Kalman filtering is presented. The mathematical formulation of a Kalman filter algorithm is rearranged to be the type of Faddeev algorithm for generalizing signal processing. The data flow mapping from the Faddeev algorithm to a two-dimensional concurrent computing structure is developed. The architecture of the resulting processor cells is regular, simple, expandable, and therefore naturally suitable for VLSI chip implementation. The computing methodology and the two-dimensional systolic arrays are useful for Kalman filter applications as well as other matrix/vector based algebraic computations.

Yeh, H.-G.↗
DOE OSTI2024

Privacy Preserving Federated Learning for Advanced Scientific Ecosystems

We present a framework to provide privacy preserving (PP) federating learning (FL) across multiple computational and experimental facilities. This work joins the compute capabilities of National Energy Research Scientific Computing Center (NERSC) and Oak Ridge National Laboratory Research Cloud (ORC) with simulated experimental data, such as those produced at the SLAC National Accelerator Laboratory and Spallation Neutron Source (SNS). We describe the software infrastructure developed to provide privacy for computational and experimental networks. We developed algorithmic privacy across the federated system by embedding database security, computation, and communication into the federation architecture, utilizing scientific tools developed by the experimental community.

Archibald, Rick [ORNL] (ORCID:0000000245389780)↗
NASA NTRS1995

The Space Cube Architecture -- A Road Map for the Future

Concepts of using modular computer system processing units using today's technology are described. Topics covered are: reasons for modularizing, reasons for stacking modules, module packaging & inter- module packaging, interconnection between modules, space-bus require- ments, gateway features, general architecture, single string and dual string architecture (and examples), and triple module redundancy.

space cube architecture modular computer system pr↗
DOE OSTI2021

Deep Optical Switching on Subpicosecond Timescales in an Amorphous Ge Metamaterial

Active nanostructured optical components show promise as potential building blocks for novel light-based computing and data processing architectures. However, nanoscale all-optical switches that have low activation powers and high-contrast ultrafast switching have been elusive so far. Here, pump–probe measurements performed on amorphous-Ge-based micro-resonator metasurfaces that exhibit strong resonant modes in the mid-infrared are reported. Relative change is observed in transmittance of ΔT/T ≈ 1 with picosecond (down to τ ≈ 0.5 ps) free carrier relaxation rates, obtained with very low pump fluences of 50 μJ cm –2 . In this work, these observations are attributed to efficient free carrier promotion, affecting light transmittance via high quality-factor optical resonances, followed by an increased electron–phonon scattering of free carriers due to the amorphous crystal structure of Ge. Full-wave simulations based on a permittivity model that describes free-carrier damping through crystal structure disorder find excellent agreement with the experimental data. These findings offer an efficient and robust platform for all-optical switching at the nanoscale.

36 MATERIALS SCIENCE↗
DOE OSTI2016

SPOKES: An end-to-end simulation facility for spectroscopic cosmological surveys

The nature of dark matter, dark energy and large-scale gravity pose some of the most pressing questions in cosmology today. These fundamental questions require highly precise measurements, and a number of wide-field spectroscopic survey instruments are being designed to meet this requirement. A key component in these experiments is the development of a simulation tool to forecast science performance, define requirement flow-downs, optimize implementation, demonstrate feasibility, and prepare for exploitation. We present SPOKES (SPectrOscopic KEn Simulation), an end-to-end simulation facility for spectroscopic cosmological surveys designed to address this challenge. SPOKES is based on an integrated infrastructure, modular function organization, coherent data handling and fast data access. These key features allow reproducibility of pipeline runs, enable ease of use and provide flexibility to update functions within the pipeline. The cyclic nature of the pipeline offers the possibility to make the science output an efficient measure for design optimization and feasibility testing. We present the architecture, first science, and computational performance results of the simulation pipeline. The framework is general, but for the benchmark tests, we use the Dark Energy Spectrometer (DESpec), one of the early concepts for the upcoming project, the Dark Energy Spectroscopic Instrument (DESI). As a result, we discuss how the SPOKES framework enables a rigorous process to optimize and exploit spectroscopic survey experiments in order to derive high-precision cosmological measurements optimally.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Graphic statics and their potential for digital design and fabrication with concrete

The paper describes the influence of the lower bound theorem of theory of plasticity on the design of structures during the last 70 years. Starting with the fundamental theorems developed in the middle of the last century, the influence of rigid plastic (discontinuous) stress fields on the design of reinforced beam, wall, slab and shell structures is worked out. The potential of graphic statics for digital design and fabrication is illustrated by the results of several research projects on form finding and design of reinforced concrete structures carried out during the last decade. It is shown that the design methods allow for simultaneous considerations about structure and architecture. This leads to leaner structure and reduced material consumption. However, while a current limitation of manufacturing objects in these ''nonstandard'' shapes is presently cost limiting, digital fabrication may change this and open a much greater scope for the use and application of graphic statics in a near future.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Hyperplane decision trees as piecewise linear surrogate models for chemical process design

Recent trends in chemical engineering research point towards an increasing reliance on data-driven modeling approaches. Neural networks, for instance, have proven to be accurate when data is plentiful and high-dimensional, but in many cases, they require computationally-intensive training procedures. Here, in this work, we describe hyperplane decision trees (HT) as a highly expressive and low-compute machine learning model architecture. These models are locally linear and have linear decision boundaries, resulting in a piecewise linear model of the data. This property allows them to be converted into mixed-integer linear constraints which can be globally optimized. Our open-source PyTorch implementation of this method is a fast, flexible, and accessible way to build accurate piecewise linear models of data.

Decision trees↗